Instructions to use kosamit/whisper-tiny-ja-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kosamit/whisper-tiny-ja-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kosamit/whisper-tiny-ja-test")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("kosamit/whisper-tiny-ja-test") model = AutoModelForSpeechSeq2Seq.from_pretrained("kosamit/whisper-tiny-ja-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kosamit/whisper-tiny-ja-test: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/kosamit/whisper-tiny-ja-test/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kosamit/whisper-tiny-ja-test/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kosamit/whisper-tiny-ja-test/resolve/main/pytorch_model.bin
151 MB
- Xet hash:
- 363c161b6c1625dfdfa42786713473ad92c556424feb81b22c500707e40b9256
- Size of remote file:
- 151 MB
- SHA256:
- 7ab43fb7bda820025d243a09a373d32341dff740ce7483ac10e8dcfc7e7871df
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